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Distributed Spectral Graph Methods for Analyzing Large-Scale Unstructured Biomedical Data

Quinn, Shannon (2014) Distributed Spectral Graph Methods for Analyzing Large-Scale Unstructured Biomedical Data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

There is an ever-expanding body of biological data, growing in size and complexity, out- stripping the capabilities of standard database tools or traditional analysis techniques. Such examples include molecular dynamics simulations, drug-target interactions, gene regulatory networks, and high-throughput imaging. Large-scale acquisition and curation biological data has already yielded results in the form of lower costs for genome sequencing and greater cov- erage in databases such as GenBank, and is viewed as the future of biocuration. The “big data” philosophy and its associated paradigms and frameworks have the potential to uncover solutions to problems otherwise intractable with more traditional investigative techniques.
Here, we focus on two biological systems whose data form large, undirected graphs. First, we develop a quantitative model of ciliary motion phenotypes, using spectral graph methods for unsupervised latent pattern discovery. Second, we apply similar techniques to identify a mapping between physiochemical structure and odor percept in human olfaction. In both cases, we experienced computational bottlenecks in our statistical machinery, necessitating the creation of a new analysis framework. At the core of this framework is a distributed hierarchical eigensolver, which we compare directly to other popular solvers. We demon- strate its essential role in enabling the discovery of novel ciliary motion phenotypes and in identifying physiochemical-perceptual associations.

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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Quinn, Shannonspq1@pitt.eduSPQ1
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairBenos, Panayiotis V.benos@pitt.eduBENOS
Thesis AdvisorChennubhotla, Chakra S.chakracs@pitt.eduCHAKRACS
Committee MemberLo, Cecilia Wcel36@pitt.eduCEL36
Committee MemberRamanathan, Arvindramanathana@ornl.gov
Committee MemberSchwartz, Russellrussells@andrew.cmu.edu
Committee MemberTaylor, Lansdltaylor@pitt.eduDLTAYLOR
Date: 17 December 2014
Date Type: Publication
Defense Date: 24 November 2014
Approval Date: 17 December 2014
Submission Date: 11 December 2014
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 181
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Computational and Systems Biology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: computational biology, computer vision, distributed computing, machine learning, olfaction, cilia
Date Deposited: 17 Dec 2015 06:00
Last Modified: 15 Nov 2016 14:26
URI: http://d-scholarship-dev.library.pitt.edu/id/eprint/23857

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